Shallow learning curve but deep capabilities and support
Rebecca T Barber, MBA, PHD | TrustRadius Reviewer
January 16, 2018

Shallow learning curve but deep capabilities and support

Score 9 out of 10
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Verified User
Review Source

Overall Satisfaction with RapidMiner Studio

Individual analysts are using it to do analysis and predictive modeling of student-related data. The organization has other tools available, but RapidMiner allows for a nice shallow learning curve. Additionally, RapidMiner is being taught in at least one online program with the intention of spreading it to other institutions and departments.
  • Work-flow visualization - the interface allows you to clearly see what the steps are and where any failures occur
  • Keeping up to date with the latest algorithms and improving the performance of those algorithms
  • Extensions that allow linking up to many of the other top tools
  • Some of the error messages are vague enough to confuse end users
  • Certain terminology used by the tool can confuse a new user
  • There are a lot of available options, many of which have only minimal documentation available. Better documentation of not just what the option is but how it might impact an analysis would help.
  • Fast development cycles means that the ability to explore and gain insight from data is far faster
  • Analysts who are not programmers can look at data independently and begin to understand the data itself
  • Shallow learning curve means that new analysts can get up to speed faster and be more productive immediately
SPSS and SAS are too expensive. Their interfaces are excellent, but the price point is quite high making them inappropriate for higher education. KNIME is my second choice tool in this space, but it doesn't have the same long established english-speaking user community as Rapidminer. That means that when I am having a technical problem I am far more likely to find a solution.
Well suited
  • Desktop analysis
  • Exploratory data analysis
  • Predictive modeling
  • Modeling that requires more than one tool
  • Data preparation
  • Workflow design and development
  • Quick turn-around development
Less well suited:
  • Multiple concurrent developers
  • Bleeding edge algorithms (they try, but the release cycle means that there is a lag)
  • Obscure or less common analyses